Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations555719
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory97.5 MiB
Average record size in memory184.0 B

Variable types

Numeric10
DateTime2
Text7
Categorical4

Alerts

Unnamed: 0 is highly overall correlated with unix_timeHigh correlation
lat is highly overall correlated with merch_lat and 1 other fieldsHigh correlation
long is highly overall correlated with merch_long and 2 other fieldsHigh correlation
merch_lat is highly overall correlated with lat and 1 other fieldsHigh correlation
merch_long is highly overall correlated with long and 2 other fieldsHigh correlation
state is highly overall correlated with lat and 4 other fieldsHigh correlation
unix_time is highly overall correlated with Unnamed: 0High correlation
zip is highly overall correlated with long and 2 other fieldsHigh correlation
is_fraud is highly imbalanced (96.3%)Imbalance
amt is highly skewed (γ1 = 37.13407684)Skewed
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
trans_num has unique valuesUnique

Reproduction

Analysis started2025-10-04 13:02:16.538574
Analysis finished2025-10-04 13:03:11.372414
Duration54.83 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct555719
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean277859
Minimum0
Maximum555718
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2025-10-04T13:03:11.458512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27785.9
Q1138929.5
median277859
Q3416788.5
95-th percentile527932.1
Maximum555718
Range555718
Interquartile range (IQR)277859

Descriptive statistics

Standard deviation160422.4
Coefficient of variation (CV)0.57735183
Kurtosis-1.2
Mean277859
Median Absolute Deviation (MAD)138930
Skewness-1.206865 × 10-15
Sum1.5441153 × 1011
Variance2.5735347 × 1010
MonotonicityStrictly increasing
2025-10-04T13:03:11.572202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5557181
 
< 0.1%
01
 
< 0.1%
11
 
< 0.1%
21
 
< 0.1%
31
 
< 0.1%
41
 
< 0.1%
51
 
< 0.1%
5557021
 
< 0.1%
5557011
 
< 0.1%
5557001
 
< 0.1%
Other values (555709)555709
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
5557181
< 0.1%
5557171
< 0.1%
5557161
< 0.1%
5557151
< 0.1%
5557141
< 0.1%
5557131
< 0.1%
5557121
< 0.1%
5557111
< 0.1%
5557101
< 0.1%
5557091
< 0.1%
Distinct544760
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
Minimum2020-06-21 12:14:25
Maximum2020-12-31 23:59:34
Invalid dates0
Invalid dates (%)0.0%
2025-10-04T13:03:11.688350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:11.807586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

cc_num
Real number (ℝ)

Distinct924
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.178387 × 1017
Minimum6.0416207 × 1010
Maximum4.9923464 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2025-10-04T13:03:11.930797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6.0416207 × 1010
5-th percentile6.3048488 × 1011
Q11.8004295 × 1014
median3.5214173 × 1015
Q34.6353306 × 1015
95-th percentile4.497914 × 1018
Maximum4.9923464 × 1018
Range4.9923463 × 1018
Interquartile range (IQR)4.4552876 × 1015

Descriptive statistics

Standard deviation1.3098366 × 1018
Coefficient of variation (CV)3.1347901
Kurtosis6.1647169
Mean4.178387 × 1017
Median Absolute Deviation (MAD)3.0764709 × 1015
Skewness2.8492038
Sum-6.7123593 × 1018
Variance1.715672 × 1036
MonotonicityNot monotonic
2025-10-04T13:03:12.043729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.538441737 × 10151474
 
0.3%
4.586810169 × 10151466
 
0.3%
4.745996322 × 10121462
 
0.3%
4.587657402 × 10181458
 
0.3%
2.242542703 × 10151428
 
0.3%
3.02730377 × 10131426
 
0.3%
3.725200498 × 10141403
 
0.3%
3.02704321 × 10131386
 
0.2%
4.364010865 × 10151375
 
0.2%
3.447098678 × 10141358
 
0.2%
Other values (914)541483
97.4%
ValueCountFrequency (%)
6.041620718 × 1010678
0.1%
6.042292873 × 1010669
0.1%
6.042309813 × 1010228
 
< 0.1%
6.042785159 × 1010215
 
< 0.1%
6.048700208 × 1010239
 
< 0.1%
6.04905963 × 1010455
0.1%
6.049559311 × 1010224
 
< 0.1%
5.018029536 × 1011635
0.1%
5.018282048 × 1011218
 
< 0.1%
5.018310822 × 1011439
0.1%
ValueCountFrequency (%)
4.992346398 × 1018863
0.2%
4.989847571 × 1018464
0.1%
4.980323468 × 1018204
 
< 0.1%
4.973530368 × 1018427
 
0.1%
4.958589672 × 1018715
0.1%
4.95682899 × 10181091
0.2%
4.906628656 × 10181071
0.2%
4.897067971 × 1018433
 
0.1%
4.890424427 × 1018693
0.1%
4.861310131 × 10181119
0.2%
Distinct693
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2025-10-04T13:03:12.274719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length43
Median length36
Mean length23.125785
Min length13

Characters and Unicode

Total characters12851438
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfraud_Kirlin and Sons
2nd rowfraud_Sporer-Keebler
3rd rowfraud_Swaniawski, Nitzsche and Welch
4th rowfraud_Haley Group
5th rowfraud_Johnston-Casper
ValueCountFrequency (%)
and203251
 
15.7%
llc41882
 
3.2%
inc39209
 
3.0%
sons31506
 
2.4%
ltd30043
 
2.3%
plc28324
 
2.2%
group21642
 
1.7%
fraud_kutch4468
 
0.3%
fraud_streich3985
 
0.3%
fraud_schaefer3973
 
0.3%
Other values (804)886783
68.5%
2025-10-04T13:03:12.606443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a1247535
 
9.7%
r1155590
 
9.0%
d916214
 
7.1%
e800035
 
6.2%
u796550
 
6.2%
n757549
 
5.9%
739347
 
5.8%
f598718
 
4.7%
_555719
 
4.3%
o484677
 
3.8%
Other values (45)4799504
37.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)12851438
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a1247535
 
9.7%
r1155590
 
9.0%
d916214
 
7.1%
e800035
 
6.2%
u796550
 
6.2%
n757549
 
5.9%
739347
 
5.8%
f598718
 
4.7%
_555719
 
4.3%
o484677
 
3.8%
Other values (45)4799504
37.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12851438
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a1247535
 
9.7%
r1155590
 
9.0%
d916214
 
7.1%
e800035
 
6.2%
u796550
 
6.2%
n757549
 
5.9%
739347
 
5.8%
f598718
 
4.7%
_555719
 
4.3%
o484677
 
3.8%
Other values (45)4799504
37.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12851438
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a1247535
 
9.7%
r1155590
 
9.0%
d916214
 
7.1%
e800035
 
6.2%
u796550
 
6.2%
n757549
 
5.9%
739347
 
5.8%
f598718
 
4.7%
_555719
 
4.3%
o484677
 
3.8%
Other values (45)4799504
37.3%

category
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
gas_transport
56370 
grocery_pos
52553 
home
52345 
shopping_pos
49791 
kids_pets
48692 
Other values (9)
295968 

Length

Max length14
Median length12
Mean length10.525528
Min length4

Characters and Unicode

Total characters5849236
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpersonal_care
2nd rowpersonal_care
3rd rowhealth_fitness
4th rowmisc_pos
5th rowtravel

Common Values

ValueCountFrequency (%)
gas_transport56370
10.1%
grocery_pos52553
9.5%
home52345
9.4%
shopping_pos49791
9.0%
kids_pets48692
8.8%
shopping_net41779
7.5%
entertainment40104
7.2%
personal_care39327
 
7.1%
food_dining39268
 
7.1%
health_fitness36674
 
6.6%
Other values (4)98816
17.8%

Length

2025-10-04T13:03:12.714238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gas_transport56370
10.1%
grocery_pos52553
9.5%
home52345
9.4%
shopping_pos49791
9.0%
kids_pets48692
8.8%
shopping_net41779
7.5%
entertainment40104
7.2%
personal_care39327
 
7.1%
food_dining39268
 
7.1%
health_fitness36674
 
6.6%
Other values (4)98816
17.8%

Most occurring characters

ValueCountFrequency (%)
s613228
10.5%
e551351
9.4%
o527045
9.0%
n511361
8.7%
p464447
 
7.9%
t461113
 
7.9%
_445821
 
7.6%
r392905
 
6.7%
i357517
 
6.1%
a285621
 
4.9%
Other values (10)1238827
21.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)5849236
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s613228
10.5%
e551351
9.4%
o527045
9.0%
n511361
8.7%
p464447
 
7.9%
t461113
 
7.9%
_445821
 
7.6%
r392905
 
6.7%
i357517
 
6.1%
a285621
 
4.9%
Other values (10)1238827
21.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5849236
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s613228
10.5%
e551351
9.4%
o527045
9.0%
n511361
8.7%
p464447
 
7.9%
t461113
 
7.9%
_445821
 
7.6%
r392905
 
6.7%
i357517
 
6.1%
a285621
 
4.9%
Other values (10)1238827
21.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5849236
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s613228
10.5%
e551351
9.4%
o527045
9.0%
n511361
8.7%
p464447
 
7.9%
t461113
 
7.9%
_445821
 
7.6%
r392905
 
6.7%
i357517
 
6.1%
a285621
 
4.9%
Other values (10)1238827
21.2%

amt
Real number (ℝ)

Skewed 

Distinct37256
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.39281
Minimum1
Maximum22768.11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2025-10-04T13:03:12.816770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.43
Q19.63
median47.29
Q383.01
95-th percentile193.051
Maximum22768.11
Range22767.11
Interquartile range (IQR)73.38

Descriptive statistics

Standard deviation156.74594
Coefficient of variation (CV)2.2588211
Kurtosis3247.084
Mean69.39281
Median Absolute Deviation (MAD)37.34
Skewness37.134077
Sum38562903
Variance24569.29
MonotonicityNot monotonic
2025-10-04T13:03:12.933060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1239
 
< 0.1%
1.14237
 
< 0.1%
1.08229
 
< 0.1%
1.31227
 
< 0.1%
1.65227
 
< 0.1%
1.03227
 
< 0.1%
1.19226
 
< 0.1%
1.07225
 
< 0.1%
1.22224
 
< 0.1%
1.09224
 
< 0.1%
Other values (37246)553434
99.6%
ValueCountFrequency (%)
1110
< 0.1%
1.01212
< 0.1%
1.02203
< 0.1%
1.03227
< 0.1%
1.04206
< 0.1%
1.05202
< 0.1%
1.06200
< 0.1%
1.07225
< 0.1%
1.08229
< 0.1%
1.09224
< 0.1%
ValueCountFrequency (%)
22768.111
< 0.1%
21437.711
< 0.1%
19364.911
< 0.1%
16837.081
< 0.1%
16339.261
< 0.1%
14637.791
< 0.1%
13149.151
< 0.1%
12969.91
< 0.1%
12882.781
< 0.1%
12882.331
< 0.1%

first
Text

Distinct341
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2025-10-04T13:03:13.240650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length9
Mean length6.0799847
Min length3

Characters and Unicode

Total characters3378763
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJeff
2nd rowJoanne
3rd rowAshley
4th rowBrian
5th rowNathan
ValueCountFrequency (%)
christopher11443
 
2.1%
robert9076
 
1.6%
jessica8655
 
1.6%
david8599
 
1.5%
michael8530
 
1.5%
james8457
 
1.5%
jennifer7241
 
1.3%
john7120
 
1.3%
mary7078
 
1.3%
william7025
 
1.3%
Other values (331)472495
85.0%
2025-10-04T13:03:13.964571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a430918
 
12.8%
e369286
 
10.9%
i265381
 
7.9%
n263215
 
7.8%
r260880
 
7.7%
l166530
 
4.9%
h148354
 
4.4%
s138914
 
4.1%
t133335
 
3.9%
o115481
 
3.4%
Other values (39)1086469
32.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)3378763
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a430918
 
12.8%
e369286
 
10.9%
i265381
 
7.9%
n263215
 
7.8%
r260880
 
7.7%
l166530
 
4.9%
h148354
 
4.4%
s138914
 
4.1%
t133335
 
3.9%
o115481
 
3.4%
Other values (39)1086469
32.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3378763
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a430918
 
12.8%
e369286
 
10.9%
i265381
 
7.9%
n263215
 
7.8%
r260880
 
7.7%
l166530
 
4.9%
h148354
 
4.4%
s138914
 
4.1%
t133335
 
3.9%
o115481
 
3.4%
Other values (39)1086469
32.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3378763
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a430918
 
12.8%
e369286
 
10.9%
i265381
 
7.9%
n263215
 
7.8%
r260880
 
7.7%
l166530
 
4.9%
h148354
 
4.4%
s138914
 
4.1%
t133335
 
3.9%
o115481
 
3.4%
Other values (39)1086469
32.2%

last
Text

Distinct471
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2025-10-04T13:03:14.318977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length10
Mean length6.1151697
Min length2

Characters and Unicode

Total characters3398316
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowElliott
2nd rowWilliams
3rd rowLopez
4th rowWilliams
5th rowMassey
ValueCountFrequency (%)
smith12146
 
2.2%
williams10056
 
1.8%
davis9524
 
1.7%
johnson8556
 
1.5%
rodriguez7485
 
1.3%
martinez6441
 
1.2%
jones5849
 
1.1%
lewis5540
 
1.0%
miller5123
 
0.9%
gonzalez5010
 
0.9%
Other values (461)479989
86.4%
2025-10-04T13:03:14.763499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e336371
 
9.9%
r282893
 
8.3%
a278699
 
8.2%
n260484
 
7.7%
o248802
 
7.3%
s209236
 
6.2%
l209106
 
6.2%
i187500
 
5.5%
t124139
 
3.7%
h98978
 
2.9%
Other values (38)1162108
34.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)3398316
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e336371
 
9.9%
r282893
 
8.3%
a278699
 
8.2%
n260484
 
7.7%
o248802
 
7.3%
s209236
 
6.2%
l209106
 
6.2%
i187500
 
5.5%
t124139
 
3.7%
h98978
 
2.9%
Other values (38)1162108
34.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3398316
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e336371
 
9.9%
r282893
 
8.3%
a278699
 
8.2%
n260484
 
7.7%
o248802
 
7.3%
s209236
 
6.2%
l209106
 
6.2%
i187500
 
5.5%
t124139
 
3.7%
h98978
 
2.9%
Other values (38)1162108
34.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3398316
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e336371
 
9.9%
r282893
 
8.3%
a278699
 
8.2%
n260484
 
7.7%
o248802
 
7.3%
s209236
 
6.2%
l209106
 
6.2%
i187500
 
5.5%
t124139
 
3.7%
h98978
 
2.9%
Other values (38)1162108
34.2%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
F
304886 
M
250833 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters555719
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowF
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
F304886
54.9%
M250833
45.1%

Length

2025-10-04T13:03:14.864517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-04T13:03:14.923758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
f304886
54.9%
m250833
45.1%

Most occurring characters

ValueCountFrequency (%)
F304886
54.9%
M250833
45.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)555719
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F304886
54.9%
M250833
45.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)555719
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F304886
54.9%
M250833
45.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)555719
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F304886
54.9%
M250833
45.1%

street
Text

Distinct924
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2025-10-04T13:03:15.171438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length35
Median length29
Mean length22.236569
Min length12

Characters and Unicode

Total characters12357284
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row351 Darlene Green
2nd row3638 Marsh Union
3rd row9333 Valentine Point
4th row32941 Krystal Mill Apt. 552
5th row5783 Evan Roads Apt. 465
ValueCountFrequency (%)
apt140506
 
6.4%
suite131549
 
5.9%
island9949
 
0.4%
michael8091
 
0.4%
islands7694
 
0.3%
station7645
 
0.3%
common7607
 
0.3%
david7377
 
0.3%
brooks7152
 
0.3%
fields7079
 
0.3%
Other values (1845)1876618
84.9%
2025-10-04T13:03:15.580386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1655548
 
13.4%
e768525
 
6.2%
a622844
 
5.0%
i554652
 
4.5%
t534046
 
4.3%
r473549
 
3.8%
n457369
 
3.7%
s442390
 
3.6%
l381006
 
3.1%
o375472
 
3.0%
Other values (52)6091883
49.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)12357284
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1655548
 
13.4%
e768525
 
6.2%
a622844
 
5.0%
i554652
 
4.5%
t534046
 
4.3%
r473549
 
3.8%
n457369
 
3.7%
s442390
 
3.6%
l381006
 
3.1%
o375472
 
3.0%
Other values (52)6091883
49.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)12357284
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1655548
 
13.4%
e768525
 
6.2%
a622844
 
5.0%
i554652
 
4.5%
t534046
 
4.3%
r473549
 
3.8%
n457369
 
3.7%
s442390
 
3.6%
l381006
 
3.1%
o375472
 
3.0%
Other values (52)6091883
49.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)12357284
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1655548
 
13.4%
e768525
 
6.2%
a622844
 
5.0%
i554652
 
4.5%
t534046
 
4.3%
r473549
 
3.8%
n457369
 
3.7%
s442390
 
3.6%
l381006
 
3.1%
o375472
 
3.0%
Other values (52)6091883
49.3%

city
Text

Distinct849
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2025-10-04T13:03:15.850935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length25
Median length21
Mean length8.6534957
Min length3

Characters and Unicode

Total characters4808912
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowColumbia
2nd rowAltonah
3rd rowBellmore
4th rowTitusville
5th rowFalmouth
ValueCountFrequency (%)
city9466
 
1.4%
west8374
 
1.2%
saint6120
 
0.9%
north6047
 
0.9%
falls5492
 
0.8%
new5015
 
0.7%
lake4840
 
0.7%
mount4723
 
0.7%
san4378
 
0.6%
springs3687
 
0.5%
Other values (876)635691
91.6%
2025-10-04T13:03:16.212131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e465724
 
9.7%
a399870
 
8.3%
n352121
 
7.3%
o350784
 
7.3%
l333877
 
6.9%
r321666
 
6.7%
i302768
 
6.3%
t257021
 
5.3%
s191281
 
4.0%
138114
 
2.9%
Other values (42)1695686
35.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)4808912
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e465724
 
9.7%
a399870
 
8.3%
n352121
 
7.3%
o350784
 
7.3%
l333877
 
6.9%
r321666
 
6.7%
i302768
 
6.3%
t257021
 
5.3%
s191281
 
4.0%
138114
 
2.9%
Other values (42)1695686
35.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4808912
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e465724
 
9.7%
a399870
 
8.3%
n352121
 
7.3%
o350784
 
7.3%
l333877
 
6.9%
r321666
 
6.7%
i302768
 
6.3%
t257021
 
5.3%
s191281
 
4.0%
138114
 
2.9%
Other values (42)1695686
35.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4808912
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e465724
 
9.7%
a399870
 
8.3%
n352121
 
7.3%
o350784
 
7.3%
l333877
 
6.9%
r321666
 
6.7%
i302768
 
6.3%
t257021
 
5.3%
s191281
 
4.0%
138114
 
2.9%
Other values (42)1695686
35.3%

state
Categorical

High correlation 

Distinct50
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
TX
40393 
NY
 
35918
PA
 
34326
CA
 
24135
OH
 
20147
Other values (45)
400800 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1111438
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSC
2nd rowUT
3rd rowNY
4th rowFL
5th rowMI

Common Values

ValueCountFrequency (%)
TX40393
 
7.3%
NY35918
 
6.5%
PA34326
 
6.2%
CA24135
 
4.3%
OH20147
 
3.6%
MI19671
 
3.5%
IL18960
 
3.4%
FL18104
 
3.3%
AL17532
 
3.2%
MO16501
 
3.0%
Other values (40)310032
55.8%

Length

2025-10-04T13:03:16.308592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tx40393
 
7.3%
ny35918
 
6.5%
pa34326
 
6.2%
ca24135
 
4.3%
oh20147
 
3.6%
mi19671
 
3.5%
il18960
 
3.4%
fl18104
 
3.3%
al17532
 
3.2%
mo16501
 
3.0%
Other values (40)310032
55.8%

Most occurring characters

ValueCountFrequency (%)
A152804
13.7%
N121925
 
11.0%
M94062
 
8.5%
I78554
 
7.1%
T65783
 
5.9%
L63584
 
5.7%
O61724
 
5.6%
C60224
 
5.4%
Y56878
 
5.1%
X40393
 
3.6%
Other values (14)315507
28.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)1111438
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A152804
13.7%
N121925
 
11.0%
M94062
 
8.5%
I78554
 
7.1%
T65783
 
5.9%
L63584
 
5.7%
O61724
 
5.6%
C60224
 
5.4%
Y56878
 
5.1%
X40393
 
3.6%
Other values (14)315507
28.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1111438
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A152804
13.7%
N121925
 
11.0%
M94062
 
8.5%
I78554
 
7.1%
T65783
 
5.9%
L63584
 
5.7%
O61724
 
5.6%
C60224
 
5.4%
Y56878
 
5.1%
X40393
 
3.6%
Other values (14)315507
28.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1111438
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A152804
13.7%
N121925
 
11.0%
M94062
 
8.5%
I78554
 
7.1%
T65783
 
5.9%
L63584
 
5.7%
O61724
 
5.6%
C60224
 
5.4%
Y56878
 
5.1%
X40393
 
3.6%
Other values (14)315507
28.4%

zip
Real number (ℝ)

High correlation 

Distinct912
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48842.628
Minimum1257
Maximum99921
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2025-10-04T13:03:16.432680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1257
5-th percentile7439
Q126292
median48174
Q372011
95-th percentile94569
Maximum99921
Range98664
Interquartile range (IQR)45719

Descriptive statistics

Standard deviation26855.283
Coefficient of variation (CV)0.54983289
Kurtosis-1.0951283
Mean48842.628
Median Absolute Deviation (MAD)23058
Skewness0.077246009
Sum2.7142776 × 1010
Variance7.2120624 × 108
MonotonicityNot monotonic
2025-10-04T13:03:16.559343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
825141589
 
0.3%
480881518
 
0.3%
341121495
 
0.3%
161141474
 
0.3%
737541470
 
0.3%
294381466
 
0.3%
463461462
 
0.3%
609581458
 
0.3%
400771428
 
0.3%
721651426
 
0.3%
Other values (902)540933
97.3%
ValueCountFrequency (%)
1257900
0.2%
1330435
0.1%
1535219
 
< 0.1%
1545444
0.1%
1612219
 
< 0.1%
18431055
0.2%
1844861
0.2%
2180219
 
< 0.1%
2630834
0.2%
2908195
 
< 0.1%
ValueCountFrequency (%)
9992114
 
< 0.1%
99783635
0.1%
99746194
 
< 0.1%
993231079
0.2%
991601332
0.2%
99113416
 
0.1%
990331188
0.2%
98836216
 
< 0.1%
98665230
 
< 0.1%
98304426
 
0.1%

lat
Real number (ℝ)

High correlation 

Distinct910
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.543253
Minimum20.0271
Maximum65.6899
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2025-10-04T13:03:16.675380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20.0271
5-th percentile29.8826
Q134.6689
median39.3716
Q341.8948
95-th percentile45.8433
Maximum65.6899
Range45.6628
Interquartile range (IQR)7.2259

Descriptive statistics

Standard deviation5.0613362
Coefficient of variation (CV)0.13131575
Kurtosis0.739401
Mean38.543253
Median Absolute Deviation (MAD)3.3472
Skewness-0.20603764
Sum21419218
Variance25.617124
MonotonicityNot monotonic
2025-10-04T13:03:16.799774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43.00481589
 
0.3%
42.51641518
 
0.3%
26.11841495
 
0.3%
41.38511474
 
0.3%
36.3851470
 
0.3%
32.54861466
 
0.3%
41.48021462
 
0.3%
41.06461458
 
0.3%
38.49211428
 
0.3%
35.57621426
 
0.3%
Other values (900)540933
97.3%
ValueCountFrequency (%)
20.0271659
0.1%
20.0827431
 
0.1%
24.65571071
0.2%
26.11841495
0.3%
26.3304199
 
< 0.1%
26.3771214
 
< 0.1%
26.42151324
0.2%
26.47221126
0.2%
26.529653
0.1%
26.6939440
 
0.1%
ValueCountFrequency (%)
65.6899194
 
< 0.1%
64.7556635
0.1%
55.473214
 
< 0.1%
48.88781332
0.2%
48.8856843
0.2%
48.8328667
0.1%
48.6669422
 
0.1%
48.60311403
0.3%
48.4786878
0.2%
48.341293
0.2%

long
Real number (ℝ)

High correlation 

Distinct910
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-90.231325
Minimum-165.6723
Maximum-67.9503
Zeros0
Zeros (%)0.0%
Negative555719
Negative (%)100.0%
Memory size4.2 MiB
2025-10-04T13:03:16.916546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-165.6723
5-th percentile-119.0825
Q1-96.798
median-87.4769
Q3-80.1752
95-th percentile-73.5365
Maximum-67.9503
Range97.722
Interquartile range (IQR)16.6228

Descriptive statistics

Standard deviation13.72178
Coefficient of variation (CV)-0.15207335
Kurtosis1.7942606
Mean-90.231325
Median Absolute Deviation (MAD)8.1276
Skewness-1.1394157
Sum-50143262
Variance188.28724
MonotonicityNot monotonic
2025-10-04T13:03:17.034772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-108.89641589
 
0.3%
-82.98321518
 
0.3%
-81.73611495
 
0.3%
-80.17521474
 
0.3%
-98.07271470
 
0.3%
-80.3071466
 
0.3%
-86.69191462
 
0.3%
-87.59171458
 
0.3%
-85.45241428
 
0.3%
-91.45391426
 
0.3%
Other values (900)540933
97.3%
ValueCountFrequency (%)
-165.6723635
0.1%
-156.292194
 
< 0.1%
-155.488431
0.1%
-155.3697659
0.1%
-133.117114
 
< 0.1%
-124.4409424
0.1%
-124.2174648
0.1%
-124.1587434
0.1%
-124.1437672
0.1%
-123.9743886
0.2%
ValueCountFrequency (%)
-67.9503842
0.2%
-68.5565453
 
0.1%
-69.2675224
 
< 0.1%
-69.4828881
0.2%
-69.9576200
 
< 0.1%
-69.96561267
0.2%
-70.239419
 
0.1%
-70.3001834
0.2%
-70.3457669
0.1%
-70.6993641
0.1%

city_pop
Real number (ℝ)

Distinct835
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88221.888
Minimum23
Maximum2906700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2025-10-04T13:03:17.143171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile139
Q1741
median2408
Q319685
95-th percentile525713
Maximum2906700
Range2906677
Interquartile range (IQR)18944

Descriptive statistics

Standard deviation300390.89
Coefficient of variation (CV)3.4049474
Kurtosis37.470475
Mean88221.888
Median Absolute Deviation (MAD)2161
Skewness5.5834146
Sum4.9026579 × 1010
Variance9.0234688 × 1010
MonotonicityNot monotonic
2025-10-04T13:03:17.261818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6062553
 
0.5%
13129222222
 
0.4%
15957972182
 
0.4%
2412045
 
0.4%
17661982
 
0.4%
21351909
 
0.3%
1981783
 
0.3%
11261783
 
0.3%
9101481765
 
0.3%
3021706
 
0.3%
Other values (825)535789
96.4%
ValueCountFrequency (%)
23866
0.2%
37456
 
0.1%
43886
0.2%
461346
0.2%
47223
 
< 0.1%
49418
 
0.1%
51454
 
0.1%
52222
 
< 0.1%
531050
0.2%
60427
 
0.1%
ValueCountFrequency (%)
29067001697
0.3%
2504700896
0.2%
2383912216
 
< 0.1%
15957972182
0.4%
15773851117
0.2%
15262061596
0.3%
1382480857
 
0.2%
13129222222
0.4%
12633211512
0.3%
12413641069
0.2%

job
Text

Distinct478
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2025-10-04T13:03:17.518063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length59
Median length38
Mean length20.244753
Min length3

Characters and Unicode

Total characters11250394
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMechanical engineer
2nd rowSales professional, IT
3rd rowLibrarian, public
4th rowSet designer
5th rowFurniture designer
ValueCountFrequency (%)
engineer56292
 
4.6%
officer47287
 
3.8%
manager26713
 
2.2%
scientist23862
 
1.9%
designer22421
 
1.8%
surveyor21226
 
1.7%
teacher16739
 
1.4%
psychologist14256
 
1.2%
research12672
 
1.0%
editor12233
 
1.0%
Other values (448)981271
79.5%
2025-10-04T13:03:18.013085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e1200919
 
10.7%
i1021383
 
9.1%
r942240
 
8.4%
a779472
 
6.9%
t765550
 
6.8%
n756706
 
6.7%
679253
 
6.0%
o641539
 
5.7%
s619943
 
5.5%
c567501
 
5.0%
Other values (43)3275888
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)11250394
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1200919
 
10.7%
i1021383
 
9.1%
r942240
 
8.4%
a779472
 
6.9%
t765550
 
6.8%
n756706
 
6.7%
679253
 
6.0%
o641539
 
5.7%
s619943
 
5.5%
c567501
 
5.0%
Other values (43)3275888
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)11250394
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1200919
 
10.7%
i1021383
 
9.1%
r942240
 
8.4%
a779472
 
6.9%
t765550
 
6.8%
n756706
 
6.7%
679253
 
6.0%
o641539
 
5.7%
s619943
 
5.5%
c567501
 
5.0%
Other values (43)3275888
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)11250394
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1200919
 
10.7%
i1021383
 
9.1%
r942240
 
8.4%
a779472
 
6.9%
t765550
 
6.8%
n756706
 
6.7%
679253
 
6.0%
o641539
 
5.7%
s619943
 
5.5%
c567501
 
5.0%
Other values (43)3275888
29.1%

dob
Date

Distinct910
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
Minimum1924-10-30 00:00:00
Maximum2005-01-29 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-04T13:03:18.200681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:18.409817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

trans_num
Text

Unique 

Distinct555719
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2025-10-04T13:03:19.245807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters17783008
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique555719 ?
Unique (%)100.0%

Sample

1st row2da90c7d74bd46a0caf3777415b3ebd3
2nd row324cc204407e99f51b0d6ca0055005e7
3rd rowc81755dbbbea9d5c77f094348a7579be
4th row2159175b9efe66dc301f149d3d5abf8c
5th row57ff021bd3f328f8738bb535c302a31b
ValueCountFrequency (%)
8be473af4f05fc6146ea55ace73e7ca21
 
< 0.1%
1765bb45b3aa3224b4cdcb6e7a96cee31
 
< 0.1%
2da90c7d74bd46a0caf3777415b3ebd31
 
< 0.1%
324cc204407e99f51b0d6ca0055005e71
 
< 0.1%
c81755dbbbea9d5c77f094348a7579be1
 
< 0.1%
2159175b9efe66dc301f149d3d5abf8c1
 
< 0.1%
57ff021bd3f328f8738bb535c302a31b1
 
< 0.1%
c65a812107218b3b5a73e508b0af66331
 
< 0.1%
e614e054201302dd73f32a3aec38bf0e1
 
< 0.1%
8b4b2ea4f0a03359f2f8fa4f418b769c1
 
< 0.1%
Other values (555709)555709
> 99.9%
2025-10-04T13:03:20.146510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
41113020
 
6.3%
b1112778
 
6.3%
11112541
 
6.3%
71112515
 
6.3%
31112419
 
6.3%
91112182
 
6.3%
81111916
 
6.3%
61111791
 
6.3%
c1111381
 
6.2%
01111306
 
6.2%
Other values (6)6661159
37.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)17783008
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
41113020
 
6.3%
b1112778
 
6.3%
11112541
 
6.3%
71112515
 
6.3%
31112419
 
6.3%
91112182
 
6.3%
81111916
 
6.3%
61111791
 
6.3%
c1111381
 
6.2%
01111306
 
6.2%
Other values (6)6661159
37.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)17783008
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
41113020
 
6.3%
b1112778
 
6.3%
11112541
 
6.3%
71112515
 
6.3%
31112419
 
6.3%
91112182
 
6.3%
81111916
 
6.3%
61111791
 
6.3%
c1111381
 
6.2%
01111306
 
6.2%
Other values (6)6661159
37.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)17783008
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
41113020
 
6.3%
b1112778
 
6.3%
11112541
 
6.3%
71112515
 
6.3%
31112419
 
6.3%
91112182
 
6.3%
81111916
 
6.3%
61111791
 
6.3%
c1111381
 
6.2%
01111306
 
6.2%
Other values (6)6661159
37.5%

unix_time
Real number (ℝ)

High correlation 

Distinct544760
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3806789 × 109
Minimum1.3718169 × 109
Maximum1.3885344 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2025-10-04T13:03:20.269114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.3718169 × 109
5-th percentile1.3725953 × 109
Q11.3760286 × 109
median1.380762 × 109
Q31.385867 × 109
95-th percentile1.3880236 × 109
Maximum1.3885344 × 109
Range16717509
Interquartile range (IQR)9838356.5

Descriptive statistics

Standard deviation5201104.1
Coefficient of variation (CV)0.0037670629
Kurtosis-1.3677361
Mean1.3806789 × 109
Median Absolute Deviation (MAD)5003435
Skewness-0.077405381
Sum7.6726948 × 1014
Variance2.7051484 × 1013
MonotonicityIncreasing
2025-10-04T13:03:20.382636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13873125994
 
< 0.1%
13869572274
 
< 0.1%
13810018694
 
< 0.1%
13874689424
 
< 0.1%
13795886203
 
< 0.1%
13744505453
 
< 0.1%
13870492123
 
< 0.1%
13882080013
 
< 0.1%
13755972403
 
< 0.1%
13755973433
 
< 0.1%
Other values (544750)555685
> 99.9%
ValueCountFrequency (%)
13718168651
< 0.1%
13718168731
< 0.1%
13718168931
< 0.1%
13718169151
< 0.1%
13718169171
< 0.1%
13718169371
< 0.1%
13718169441
< 0.1%
13718169501
< 0.1%
13718169701
< 0.1%
13718169711
< 0.1%
ValueCountFrequency (%)
13885343741
< 0.1%
13885343641
< 0.1%
13885343551
< 0.1%
13885343491
< 0.1%
13885343471
< 0.1%
13885343141
< 0.1%
13885342841
< 0.1%
13885342761
< 0.1%
13885342701
< 0.1%
13885342381
< 0.1%

merch_lat
Real number (ℝ)

High correlation 

Distinct546490
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.542798
Minimum19.027422
Maximum66.679297
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2025-10-04T13:03:20.503389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum19.027422
5-th percentile29.759168
Q134.755302
median39.376593
Q341.954163
95-th percentile45.99952
Maximum66.679297
Range47.651875
Interquartile range (IQR)7.1988615

Descriptive statistics

Standard deviation5.0958293
Coefficient of variation (CV)0.13221223
Kurtosis0.72289788
Mean38.542798
Median Absolute Deviation (MAD)3.372633
Skewness-0.20262611
Sum21418965
Variance25.967476
MonotonicityNot monotonic
2025-10-04T13:03:20.633057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.7795654
 
< 0.1%
40.9525444
 
< 0.1%
43.0910153
 
< 0.1%
33.8257143
 
< 0.1%
39.970873
 
< 0.1%
39.6612883
 
< 0.1%
41.9605963
 
< 0.1%
40.0254013
 
< 0.1%
39.5933913
 
< 0.1%
40.7519753
 
< 0.1%
Other values (546480)555687
> 99.9%
ValueCountFrequency (%)
19.0274221
< 0.1%
19.0278491
< 0.1%
19.0326891
< 0.1%
19.0381071
< 0.1%
19.0395321
< 0.1%
19.0398621
< 0.1%
19.042321
< 0.1%
19.0447471
< 0.1%
19.0451991
< 0.1%
19.0461131
< 0.1%
ValueCountFrequency (%)
66.6792971
< 0.1%
66.6747141
< 0.1%
66.671541
< 0.1%
66.6693561
< 0.1%
66.6681671
< 0.1%
66.6604541
< 0.1%
66.6503881
< 0.1%
66.6467531
< 0.1%
66.6460511
< 0.1%
66.6443971
< 0.1%

merch_long
Real number (ℝ)

High correlation 

Distinct551770
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-90.23138
Minimum-166.67157
Maximum-66.952026
Zeros0
Zeros (%)0.0%
Negative555719
Negative (%)100.0%
Memory size4.2 MiB
2025-10-04T13:03:20.755054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-166.67157
5-th percentile-119.25804
Q1-96.905129
median-87.445204
Q3-80.264637
95-th percentile-73.387755
Maximum-66.952026
Range99.719549
Interquartile range (IQR)16.640492

Descriptive statistics

Standard deviation13.733071
Coefficient of variation (CV)-0.15219839
Kurtosis1.7905728
Mean-90.23138
Median Absolute Deviation (MAD)8.212831
Skewness-1.1368095
Sum-50143293
Variance188.59723
MonotonicityNot monotonic
2025-10-04T13:03:20.870081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-87.0095343
 
< 0.1%
-78.0960033
 
< 0.1%
-74.3003983
 
< 0.1%
-97.4836433
 
< 0.1%
-83.5361523
 
< 0.1%
-82.2688453
 
< 0.1%
-86.3654423
 
< 0.1%
-87.2561183
 
< 0.1%
-77.2442713
 
< 0.1%
-82.3579413
 
< 0.1%
Other values (551760)555689
> 99.9%
ValueCountFrequency (%)
-166.6715751
< 0.1%
-166.6706851
< 0.1%
-166.6700061
< 0.1%
-166.669911
< 0.1%
-166.6698121
< 0.1%
-166.6587971
< 0.1%
-166.6536051
< 0.1%
-166.6470521
< 0.1%
-166.6466431
< 0.1%
-166.6465071
< 0.1%
ValueCountFrequency (%)
-66.9520261
< 0.1%
-66.9523521
< 0.1%
-66.9556021
< 0.1%
-66.9573641
< 0.1%
-66.9594981
< 0.1%
-66.9607451
< 0.1%
-66.9705251
< 0.1%
-66.9735271
< 0.1%
-66.9787451
< 0.1%
-66.9809591
< 0.1%

is_fraud
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
0
553574 
1
 
2145

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters555719
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0553574
99.6%
12145
 
0.4%

Length

2025-10-04T13:03:20.966713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-04T13:03:21.021593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0553574
99.6%
12145
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0553574
99.6%
12145
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)555719
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0553574
99.6%
12145
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)555719
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0553574
99.6%
12145
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)555719
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0553574
99.6%
12145
 
0.4%

Interactions

2025-10-04T13:03:05.996859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:51.783880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:53.774201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:55.101730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:56.398388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:57.757559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:59.286057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:00.669811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:02.052959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:03.970362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:06.216879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:51.961098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:53.917218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:55.247625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:56.539757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:58.099993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:59.427449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:00.810691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:02.200211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:04.211041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:06.450210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:52.156502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:54.047547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:55.376697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:56.676251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:58.235597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:59.569479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:00.944934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:02.335235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:04.424690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:06.653810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:52.327684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:54.188697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:55.501104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:56.808306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:58.372920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:59.707999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:01.078412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:02.475171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:04.602927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:06.902022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:52.519459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:54.319824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:55.626434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:56.939803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:58.497438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:59.847474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:01.212928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:02.623643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:04.820562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:07.146819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:52.703196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:54.447321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:55.750826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:57.068897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:58.621973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:59.980907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:01.344302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:02.756072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:05.019733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:07.366768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:52.914175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:54.572469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:55.871809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:57.200352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:58.753706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:00.112432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:01.491235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:02.884760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:05.221364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:07.640428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:53.113116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:54.706458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:56.002155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:57.346427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:58.892313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:00.257023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:01.630310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:03.024418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:05.436664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:07.796090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:53.322558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:54.839389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:56.132066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:57.485159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:59.026354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:00.403122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:01.770627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:03.499718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:05.624222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:07.927639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:53.451893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:54.972064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:56.259812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:57.616194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:02:59.156667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:00.544666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:01.917035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:03.724189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-04T13:03:05.793272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-04T13:03:21.079088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Unnamed: 0amtcategorycc_numcity_popgenderis_fraudlatlongmerch_latmerch_longstateunix_timezip
Unnamed: 01.000-0.0010.0060.001-0.0010.0000.023-0.000-0.0020.000-0.0020.0001.0000.001
amt-0.0011.0000.021-0.001-0.0240.0030.0000.014-0.0010.014-0.0010.004-0.0010.001
category0.0060.0211.0000.0070.0140.0550.0580.0110.0090.0110.0090.0190.0040.010
cc_num0.001-0.0010.0071.0000.0510.0530.004-0.001-0.013-0.002-0.0130.2380.0010.013
city_pop-0.001-0.0240.0140.0511.0000.0900.008-0.2610.088-0.2600.0870.315-0.001-0.042
gender0.0000.0030.0550.0530.0901.0000.0000.1240.0910.1120.0830.2550.0010.115
is_fraud0.0230.0000.0580.0040.0080.0001.0000.0810.0810.0810.0810.0270.0220.009
lat-0.0000.0140.011-0.001-0.2610.1240.0811.0000.1050.9910.1030.797-0.000-0.162
long-0.002-0.0010.009-0.0130.0880.0910.0810.1051.0000.1050.9980.864-0.002-0.959
merch_lat0.0000.0140.011-0.002-0.2600.1120.0810.9910.1051.0000.1030.7720.000-0.162
merch_long-0.002-0.0010.009-0.0130.0870.0830.0810.1030.9980.1031.0000.832-0.002-0.957
state0.0000.0040.0190.2380.3150.2550.0270.7970.8640.7720.8321.0000.0000.963
unix_time1.000-0.0010.0040.001-0.0010.0010.022-0.000-0.0020.000-0.0020.0001.0000.001
zip0.0010.0010.0100.013-0.0420.1150.009-0.162-0.959-0.162-0.9570.9630.0011.000

Missing values

2025-10-04T13:03:08.417312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-04T13:03:09.470887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0trans_date_trans_timecc_nummerchantcategoryamtfirstlastgenderstreetcitystateziplatlongcity_popjobdobtrans_numunix_timemerch_latmerch_longis_fraud
002020-06-21 12:14:252291163933867244fraud_Kirlin and Sonspersonal_care2.86JeffElliottM351 Darlene GreenColumbiaSC2920933.9659-80.9355333497Mechanical engineer1968-03-192da90c7d74bd46a0caf3777415b3ebd3137181686533.986391-81.2007140
112020-06-21 12:14:333573030041201292fraud_Sporer-Keeblerpersonal_care29.84JoanneWilliamsF3638 Marsh UnionAltonahUT8400240.3207-110.4360302Sales professional, IT1990-01-17324cc204407e99f51b0d6ca0055005e7137181687339.450498-109.9604310
222020-06-21 12:14:533598215285024754fraud_Swaniawski, Nitzsche and Welchhealth_fitness41.28AshleyLopezF9333 Valentine PointBellmoreNY1171040.6729-73.536534496Librarian, public1970-10-21c81755dbbbea9d5c77f094348a7579be137181689340.495810-74.1961110
332020-06-21 12:15:153591919803438423fraud_Haley Groupmisc_pos60.05BrianWilliamsM32941 Krystal Mill Apt. 552TitusvilleFL3278028.5697-80.819154767Set designer1987-07-252159175b9efe66dc301f149d3d5abf8c137181691528.812398-80.8830610
442020-06-21 12:15:173526826139003047fraud_Johnston-Caspertravel3.19NathanMasseyM5783 Evan Roads Apt. 465FalmouthMI4963244.2529-85.01701126Furniture designer1955-07-0657ff021bd3f328f8738bb535c302a31b137181691744.959148-85.8847340
552020-06-21 12:15:3730407675418785fraud_Daugherty LLCkids_pets19.55DanielleEvansF76752 David Lodge Apt. 064BreesportNY1481642.1939-76.7361520Psychotherapist1991-10-13798db04aaceb4febd084f1a7c404da93137181693741.747157-77.5841970
662020-06-21 12:15:44213180742685905fraud_Romaguera Ltdhealth_fitness133.93KaylaSuttonF010 Weaver LandCarlottaCA9552840.5070-123.97431139Therapist, occupational1951-01-1517003d7ce534440eadb10c4750e020e5137181694441.499458-124.8887290
772020-06-21 12:15:503589289942931264fraud_Reichel LLCpersonal_care10.37PaulaEstradaF350 Stacy GlensSpencerSD5737443.7557-97.5936343Development worker, international aid1972-03-058be473af4f05fc6146ea55ace73e7ca2137181695044.495498-97.7284530
882020-06-21 12:16:103596357274378601fraud_Goyette, Howell and Colliershopping_pos4.37DavidEverettM4138 David FallMorrisdalePA1685841.0001-78.23573688Advice worker1973-05-2771a1da150d1ce510193d7622e08e784e137181697041.546067-78.1202380
992020-06-21 12:16:113546897637165774fraud_Kilback Groupfood_dining66.54KaylaObrienF7921 Robert Port Suite 343Prairie HillTX7667831.6591-96.8094263Barrister1956-05-30a7915132c7c4240996ba03a47f81e3bd137181697131.782919-96.3661850
Unnamed: 0trans_date_trans_timecc_nummerchantcategoryamtfirstlastgenderstreetcitystateziplatlongcity_popjobdobtrans_numunix_timemerch_latmerch_longis_fraud
5557095557092020-12-31 23:57:1830344654314976fraud_Larkin, Stracke and Greenfelderentertainment46.71ChristineJohnsonF8011 Chapman Tunnel Apt. 568Blairsden-GraeagleCA9610339.8127-120.64051725Chartered legal executive (England and Wales)1967-05-27a7105564935ea3977dc61ff9ced3bf5e138853423838.963543-120.4571210
5557105557102020-12-31 23:57:503524574586339330fraud_Heathcote, Yost and Kertzmannshopping_net29.56AshleyCabreraF94225 Smith Springs Apt. 617Vero BeachFL3296027.6330-80.4031105638Librarian, public1986-05-079fc9f6f9be3182d519a61a119cf97199138853427027.593881-80.8550920
5557115557112020-12-31 23:57:56341546199006537fraud_Schmidt-Larkinhome12.68MarkBrownM8580 Moore CoveWalesAK9978364.7556-165.6723145Administrator, education1939-11-09a8310343c189e4a5b6316050d2d6b014138853427665.623593-165.1860330
5557125557122020-12-31 23:58:04501802953619fraud_Pouros, Walker and Spencerkids_pets13.02RobertFloresM3277 Fields Meadows Apt. 790GreenviewCA9603741.5403-122.9366308Call centre manager1958-09-20bd7071fd5c9510a5594ee196368ac80e138853428441.973127-123.5530320
5557135557132020-12-31 23:58:343523843138706408fraud_Prosacco, Kreiger and Kovacekhome17.00GraceWilliamsF28812 Charles Mill Apt. 628PlantersvilleAL3675832.6176-86.94751412Drilling engineer1970-11-206d04313bfe4b661b8ca2b6a499a320fe138853431432.164145-87.5396690
5557145557142020-12-31 23:59:0730560609640617fraud_Reilly and Sonshealth_fitness43.77MichaelOlsonM558 Michael EstatesLurayMO6345340.4931-91.8912519Town planner1966-02-139b1f753c79894c9f4b71f04581835ada138853434739.946837-91.3333310
5557155557152020-12-31 23:59:093556613125071656fraud_Hoppe-Parisiankids_pets111.84JoseVasquezM572 Davis MountainsLake JacksonTX7756629.0393-95.440128739Futures trader1999-12-272090647dac2c89a1d86c514c427f5b91138853434929.661049-96.1866330
5557165557162020-12-31 23:59:156011724471098086fraud_Rau-Robelkids_pets86.88AnnLawsonF144 Evans Islands Apt. 683BurbankWA9932346.1966-118.90173684Musician1981-11-296c5b7c8add471975aa0fec023b2e8408138853435546.658340-119.7150540
5557175557172020-12-31 23:59:244079773899158fraud_Breitenberg LLCtravel7.99EricPrestonM7020 Doyle Stream Apt. 951MesaID8364344.6255-116.4493129Cartographer1965-12-1514392d723bb7737606b2700ac791b7aa138853436444.470525-117.0808880
5557185557182020-12-31 23:59:344170689372027579fraud_Dare-Marvinentertainment38.13SamuelFreyM830 Myers Plaza Apt. 384EdmondOK7303435.6665-97.4798116001Media buyer1993-05-101765bb45b3aa3224b4cdcb6e7a96cee3138853437436.210097-97.0363720